Title :
Short term load forecasting of Iran national power system using artificial neural network generation two
Author :
Barzamini, R. ; Menhaj, M.B. ; Kamalvand, Sh. ; Tajbakhsh, A.
Author_Institution :
Amirkabir Univ. of Technol., Tehran
Abstract :
This paper presents a neuro-based short term load forecasting (STLF) method for Iran national power system (INPS) and its regions. This is an improved version of the one given in [1]. The architecture of the proposed network is a three-layer feed forward neural network whose parameters are tuned by Levenberg-Marquardt BP (LMBP) augmented by an early stopping (ES) method tried out for increasing the speed of convergence. Instead of seasonal training, an input as a month indicator is added to the input vectors. The short term load forecasting simulator developed so far presents satisfactory and better results for one hour up to a week prediction of INPS loads and region of INPS, Bakhtar Region Electric Co (BREC).
Keywords :
backpropagation; feedforward neural nets; load forecasting; power engineering computing; Bakhtar Region Electric Co; Iran national power system; Levenberg-Marquardt backpropagation; artificial neural network generation; early stopping method; neuro-based short term load forecasting; three-layer feed forward neural network; Artificial neural networks; Convergence; Feedforward neural networks; Feeds; Indium phosphide; Load forecasting; Neural networks; Power generation; Power systems; Predictive models;
Conference_Titel :
Power Tech, 2005 IEEE Russia
Conference_Location :
St. Petersburg
Print_ISBN :
978-5-93208-034-4
Electronic_ISBN :
978-5-93208-034-4
DOI :
10.1109/PTC.2005.4524536
